Why AI matters at this scale

Alter Trading is a 125-year-old, St. Louis-based leader in the global metals recycling and trading industry. With over 1,000 employees and operations spanning procurement, processing, and logistics, the company acts as a critical link between scrap generators and steel mills or foundries. Its business is fundamentally driven by commodity price volatility, complex logistics, and the efficient processing of heterogeneous material streams. At its size—a mid-market enterprise with a large physical footprint—operational efficiency and margin precision are paramount. The sector, however, remains traditionally low-tech and relationship-driven, creating a significant opportunity for data-savvy competitors.

For a company of Alter's scale and vintage, AI is not about futuristic automation but about harnessing decades of latent operational data to make better, faster decisions. The sheer volume of transactions, the complexity of routing trucks and railcars, and the capital intensity of processing equipment generate massive datasets. Currently, this data is likely underutilized, trapped in legacy ERP systems or spreadsheets. AI provides the toolset to unlock this value, transforming intuition-based trading and maintenance into predictive, optimized operations. In a margin-compressed industry, a few percentage points of improvement in logistics costs, material yield, or equipment uptime translate directly to millions in annual EBITDA.

Concrete AI Opportunities with ROI Framing

1. Predictive Pricing and Trading Intelligence: By applying machine learning to historical purchase/sale data, global commodity feeds, and macroeconomic indicators, Alter can build models that forecast short-term price movements for specific scrap grades. This allows traders to buy more aggressively in anticipation of price rises and hedge more effectively. The ROI is direct: a 1-2% improvement in average margin capture across billions in annual volume is transformative.

2. Dynamic Logistics Optimization: Machine learning algorithms can continuously optimize collection and delivery routes by ingesting real-time data on traffic, fuel prices, truck capacity, and customer demand. For a fleet managing thousands of shipments, this reduces empty miles, cuts fuel consumption, and improves asset utilization. The payoff is a 10-15% reduction in logistics costs, a major line-item expense.

3. Predictive Maintenance for Capital Assets: Shredders, balers, and material handlers are expensive and prone to unplanned failure. Installing IoT sensors and applying AI to predict failures based on vibration, temperature, and operational hours data shifts maintenance from reactive to planned. This minimizes catastrophic downtime that can cost tens of thousands per hour in lost processing capacity, extending equipment life and reducing repair costs.

Deployment Risks Specific to This Size Band

As a company with 1,001-5,000 employees, Alter Trading faces distinct adoption risks. Its operations are likely decentralized across multiple yards and offices, creating data silos and inconsistent processes that complicate AI integration. The company has the revenue to fund pilots but may lack the centralized data science talent of a Fortune 500 firm, risking reliance on external consultants without deep domain knowledge. Furthermore, the cultural shift from decades of experience-based decision-making to data-driven recommendations can meet resistance from veteran traders and yard managers. Successful deployment requires strong executive sponsorship, starting with focused pilots that demonstrate clear, quick wins to build organizational trust, and investing in upskilling existing staff rather than solely hiring external tech talent.